Learning to Ask: Information Acquisition for SLM-LLM Collaboration, under a budget
Collaboration between a small language model (SLM) and a large language model (LLM) offers an opportunity to combine the efficiency of smaller models with the strong reasoning capabilities of larger ones. Existing approaches primarily frame such collaboration as a computation allocation problem, determining which model should handle each portion of the reasoning process. In black-box API-based settings, however, this paradigm can be inefficient due to coarse-grained delegation or repeated transmission of context across model switches. In this work, we instead formulate SLM-LLM collaboration as an information acquisition problem, under an API budget constraint. The SLM remains the primary ...
| Authors | Yongjun Kim, Xiaoxiao Li, Jaeho Lee |
|---|---|
| Primary category | cs.AI |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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